l2正则化代码python

以下是Python中使用L2正则化的示例代码：

import tensorflow as tf

# 定义输入和输出
x = tf.placeholder(tf.float32, [None, input_size])
y = tf.placeholder(tf.float32, [None, output_size])

# 定义模型参数
W = tf.Variable(tf.zeros([input_size, output_size]))
b = tf.Variable(tf.zeros([output_size]))

# 定义L2正则化系数
l2_reg = tf.constant(0.001)

# 定义模型
y_pred = tf.matmul(x, W) + b

# 定义损失函数，包括L2正则化项
loss = tf.reduce_mean(tf.square(y - y_pred)) + l2_reg * tf.nn.l2_loss(W)

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

其中，`tf.nn.l2_loss(W)`计算权重W的L2范数的平方，`l2_reg`为L2正则化系数，`loss`为损失函数，包括L2正则化项。在优化器中使用`minimize(loss)`来最小化损失函数。

相关问题

L2正则化python代码

### Python 实现 L2 正则化

为了减少过拟合现象，在神经网络模型中加入L2正则化是一种常见做法。通过向损失函数添加权重参数平方和的形式来惩罚较大的权值，从而使得模型更加泛化[^4]。

下面是一个简单的Python代码片段用于展示如何在计算成本以及反向传播过程中融入L2正则化：

import numpy as np

def compute_cost_with_regularization(A3, Y, parameters, lambd):
    """
    Implement the cost function with L2 regularization
    
    Arguments:
    A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)
    Y -- "true" labels vector, of shape (output size, number of examples)
    parameters -- python dictionary containing parameters of the model
    lambd -- regularization hyperparameter, scalar
    
    Returns:
    cost -- value of the regularized loss function
    """
    m = Y.shape[1]
    W1 = parameters["W1"]
    W2 = parameters["W2"]
    W3 = parameters["W3"]

    cross_entropy_cost = -np.sum(np.multiply(Y,np.log(A3)) + np.multiply(1-Y, np.log(1-A3))) / m  # This gives you the cross-entropy part of the cost
    L2_regularization_cost = (lambd/(2*m))*(np.sum(np.square(W1))+np.sum(np.square(W2))+np.sum(np.square(W3)))
    
    cost = cross_entropy_cost + L2_regularization_cost
    
    return cost


def backward_propagation_with_regularization(X, Y, cache, lambd):
    """
    Implements the backward propagation of our baseline model to which we added an L2 regularization.
    
    Arguments:
    X -- input dataset, of shape (input size, number of examples)
    Y -- "true" labels vector, of shape (output size, number of examples)
    cache -- cache output from forward_propagation()
    lambd -- regularization hyperparameter, scalar
    
    Returns:
    gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables
    """
    m = X.shape[1]
    (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache
    
    dZ3 = A3 - Y
    dW3 = 1./m * np.dot(dZ3, A2.T) + (lambd/m)*W3
    db3 = 1./m * np.sum(dZ3, axis=1, keepdims=True)

    dA2 = np.dot(W3.T, dZ3)
    dZ2 = np.multiply(dA2, np.int64(A2 > 0))
    dW2 = 1./m * np.dot(dZ2, A1.T) + (lambd/m)*W2
    db2 = 1./m * np.sum(dZ2, axis=1, keepdims=True)

    dA1 = np.dot(W2.T, dZ2)
    dZ1 = np.multiply(dA1, np.int64(A1 > 0))
    dW1 = 1./m * np.dot(dZ1, X.T) + (lambd/m)*W1
    db1 = 1./m * np.sum(dZ1, axis=1, keepdims=True)
    
    gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,"dA2": dA2,
                 "dZ2": dZ2, "dW2": dW2, "db2": db2, "dA1": dA1, 
                 "dZ1": dZ1, "dW1": dW1, "db1": db1}
    
    return gradients

上述代码展示了两个主要功能：`compute_cost_with_regularization()` 和 `backward_propagation_with_regularization()` ，它们分别负责带有L2正则化的代价函数计算与反向传播过程中的梯度更新操作[^1]。

l2正则化 python代码实现

l2正则化是一种常用于机器学习模型中的正则化方法，可以帮助减小模型的复杂度并防止过拟合。在Python中，我们可以使用不同的库来实现l2正则化，比如NumPy、scikit-learn等。下面是一个使用NumPy库实现l2正则化的示例代码：

import numpy as np

def l2_regularization(weights, lambda_val):
    regularization_term = lambda_val * np.sum(weights ** 2)
    return regularization_term

# 示例用法
weights = np.array([1, 2, 3, 4, 5])
lambda_val = 0.1
regularization_term = l2_regularization(weights, lambda_val)
print("L2 regularization term:", regularization_term)

在以上代码中，`l2_regularization`函数接受两个参数：`weights`表示待正则化的权重向量，`lambda_val`表示正则化的参数。函数中计算了l2正则化项，并返回结果。

希望以上代码对你有所帮助！如果有任何问题，请随时提问。